Search (8 results, page 1 of 1)

0.016057936 = product of:
  0.080289684 = sum of:
    0.080289684 = weight(_text_:retrieval in 1951) [ClassicSimilarity], result of:
      0.080289684 = score(doc=1951,freq=14.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.61896384 = fieldWeight in 1951, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1951)
  0.2 = coord(1/5)

Abstract

Describes experiments in the retrieval of spoken documents in multimedia systems. Speech documents pose a particular problem for retrieval since their words as well as contents are unknown. Addresses this problem, for a video mail application, by combining state of the art speech recognition with established document retrieval technologies so as to provide an effective and efficient retrieval tool. Tests with a small spoken message collection show that retrieval precision for the spoken file can reach 90% of that obtained when the same file is used, as a benchmark, in text transcription form

Footnote

Wiederabdruck in: Readings in informatio retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.493-502.

0.01213866 = product of:
  0.060693298 = sum of:
    0.060693298 = weight(_text_:retrieval in 4144) [ClassicSimilarity], result of:
      0.060693298 = score(doc=4144,freq=2.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.46789268 = fieldWeight in 4144, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.109375 = fieldNorm(doc=4144)
  0.2 = coord(1/5)

0.010404565 = product of:
  0.052022826 = sum of:
    0.052022826 = weight(_text_:retrieval in 3098) [ClassicSimilarity], result of:
      0.052022826 = score(doc=3098,freq=2.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.40105087 = fieldWeight in 3098, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=3098)
  0.2 = coord(1/5)

Source

The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman

0.009809518 = product of:
  0.04904759 = sum of:
    0.04904759 = weight(_text_:retrieval in 4811) [ClassicSimilarity], result of:
      0.04904759 = score(doc=4811,freq=4.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.37811437 = fieldWeight in 4811, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=4811)
  0.2 = coord(1/5)

Abstract

Presents a view of the scope of artificial intelligence (AI) in information retrieval (IR). Considers potential roles of AI and IR, evaluating AI from a realistic point od view and within a wide information management potential, not just because AI is itself insufficiently developed, but because many information management tasks are properly shallow information processing ones. There is nevertheless an important place for specific applications of AI or AI-derived technology when particular constraints can be placed on the information management tasks involved

0.009809518 = product of:
  0.04904759 = sum of:
    0.04904759 = weight(_text_:retrieval in 1916) [ClassicSimilarity], result of:
      0.04904759 = score(doc=1916,freq=4.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.37811437 = fieldWeight in 1916, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=1916)
  0.2 = coord(1/5)

Abstract

Discusses the TREC programme as a major enterprise in information retrieval research. It reviews its structure as an evaluation exercise, characterises the methods of indexing and retrieval being tested within it in terms of the approaches to system performance factors these represent; analyses the test results for solid, overall conclusions that can be drawn from them; and, in the light of the particular features of the test data, assesses TREC both for generally applicable findings that emerge from it and for directions it offers for future research

0.0096938815 = product of:
  0.048469406 = sum of:
    0.048469406 = weight(_text_:retrieval in 4532) [ClassicSimilarity], result of:
      0.048469406 = score(doc=4532,freq=10.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.37365708 = fieldWeight in 4532, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4532)
  0.2 = coord(1/5)

Abstract

This technical note describes straightforward techniques for document indexing and retrieval that have been solidly established through extensive testing and are easy to apply. They are useful for many different types of text material, are viable for very large files, and have the advantage that they do not require special skills or training for searching, but are easy for end users. The document and text retrieval methods described here have a sound theoretical basis, are well established by extensive testing, and the ideas involved are now implemented in some commercial retrieval systems. Testing in the last few years has, in particular, shown that the methods presented here work very well with full texts, not only title and abstracts, and with large files of texts containing three quarters of a million documents. These tests, the TREC Tests (see Harman 1993 - 1997; IP&M 1995), have been rigorous comparative evaluations involving many different approaches to information retrieval. These techniques depend an the use of simple terms for indexing both request and document texts; an term weighting exploiting statistical information about term occurrences; an scoring for request-document matching, using these weights, to obtain a ranked search output; and an relevance feedback to modify request weights or term sets in iterative searching. The normal implementation is via an inverted file organisation using a term list with linked document identifiers, plus counting data, and pointers to the actual texts. The user's request can be a word list, phrases, sentences or extended text.

0.009010619 = product of:
  0.04505309 = sum of:
    0.04505309 = weight(_text_:retrieval in 580) [ClassicSimilarity], result of:
      0.04505309 = score(doc=580,freq=6.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.34732026 = fieldWeight in 580, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=580)
  0.2 = coord(1/5)

Abstract

This paper discusses the Text REtrieval Conferences (TREC) programme as a major enterprise in information retrieval research. It reviews its structure as an evaluation exercise, characterises the methods of indexing and retrieval being tested within its terms of the approaches to system performance factors these represent; analyses the test results for solid, overall conclusions that can be drawn from them; and, in the light of the particular features of the test data, assesses TREC both for generally applicable findings that emerge from it and for directions it offers for future research

0.0069363764 = product of:
  0.034681883 = sum of:
    0.034681883 = weight(_text_:retrieval in 575) [ClassicSimilarity], result of:
      0.034681883 = score(doc=575,freq=2.0), product of:
        0.12971628 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042882618 = queryNorm
        0.26736724 = fieldWeight in 575, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=575)
  0.2 = coord(1/5)